Python + JavaScript ML入門 | ML Learning Hub
Python + JavaScript for Machine Learning
Experience the power of hybrid ML development with instant results
🔗 Language Synergy
🐍 Python
Training Powerhouse
- Rich ecosystem (NumPy, Pandas, TensorFlow)
- Advanced model architectures
- Efficient data processing
⚡ JavaScript
Deployment Excellence
- Browser-native execution
- Real-time user interaction
- No server dependency
🚀 Instant ML Demos
🖼️ Lightweight Image Analysis
Upload any image for instant analysis using browser-based feature detection
💭 Sentiment Analysis
Enter text to analyze its emotional tone instantly
📈 Live Linear Regression Training
Click anywhere to add data points and watch the model learn in real-time
📊 3D Learning Visualization
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100
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💻 Implementation Examples
🐍 Python Training Code
# Python: Model Training
import tensorflow as tf
from sklearn.model_selection import train_test_split
# Load and preprocess data
model = tf.keras.Sequential([
tf.keras.layers.Dense(64, activation='relu'),
tf.keras.layers.Dense(32, activation='relu'),
tf.keras.layers.Dense(1, activation='sigmoid')
])
# Compile and train
model.compile(optimizer='adam',
loss='binary_crossentropy',
metrics=['accuracy'])
model.fit(X_train, y_train, epochs=100)
# Save for web deployment
model.save('model.h5')
⚡ JavaScript Deployment
// JavaScript: Model Loading & Inference
const model = await tf.loadLayersModel('/model.json');
// Real-time prediction
function predict(inputData) {
const tensor = tf.tensor2d([inputData]);
const prediction = model.predict(tensor);
return prediction.dataSync()[0];
}
// Live update UI
document.getElementById('input').addEventListener('input', (e) => {
const result = predict(processInput(e.target.value));
displayResult(result);
});
// No server needed!
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